Literature DB >> 14560049

PROSPECT II: protein structure prediction program for genome-scale applications.

Dongsup Kim1, Dong Xu, Jun-tao Guo, Kyle Ellrott, Ying Xu.   

Abstract

A new method for fold recognition is developed and added to the general protein structure prediction package PROSPECT (http://compbio.ornl.gov/PROSPECT/). The new method (PROSPECT II) has four key features. (i) We have developed an efficient way to utilize the evolutionary information for evaluating the threading potentials including singleton and pairwise energies. (ii) We have developed a two-stage threading strategy: (a) threading using dynamic programming without considering the pairwise energy and (b) fold recognition considering all the energy terms, including the pairwise energy calculated from the dynamic programming threading alignments. (iii) We have developed a combined z-score scheme for fold recognition, which takes into consideration the z-scores of each energy term. (iv) Based on the z-scores, we have developed a confidence index, which measures the reliability of a prediction and a possible structure-function relationship based on a statistical analysis of a large data set consisting of threadings of 600 query proteins against the entire FSSP templates. Tests on several benchmark sets indicate that the evolutionary information and other new features of PROSPECT II greatly improve the alignment accuracy. We also demonstrate that the performance of PROSPECT II on fold recognition is significantly better than any other method available at all levels of similarity. Improvement in the sensitivity of the fold recognition, especially at the superfamily and fold levels, makes PROSPECT II a reliable and fully automated protein structure and function prediction program for genome-scale applications.

Mesh:

Year:  2003        PMID: 14560049     DOI: 10.1093/protein/gzg081

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  29 in total

1.  Protein structure prediction using sparse dipolar coupling data.

Authors:  Youxing Qu; Jun-tao Guo; Victor Olman; Ying Xu
Journal:  Nucleic Acids Res       Date:  2004-01-26       Impact factor: 16.971

2.  PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction.

Authors:  Jun-tao Guo; Kyle Ellrott; Won Jae Chung; Dong Xu; Serguei Passovets; Ying Xu
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

3.  CONTSOR--a new knowledge-based fold recognition potential, based on side chain orientation and contacts between residue terminal groups.

Authors:  Boris Vishnepolsky; Malak Pirtskhalava
Journal:  Protein Sci       Date:  2011-11-23       Impact factor: 6.725

4.  Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments.

Authors:  Hongyi Zhou; Yaoqi Zhou
Journal:  Proteins       Date:  2005-02-01

5.  Fold assessment for comparative protein structure modeling.

Authors:  Francisco Melo; Andrej Sali
Journal:  Protein Sci       Date:  2007-09-28       Impact factor: 6.725

6.  Computation of 3D queries for ROCS based virtual screens.

Authors:  Gregory J Tawa; J Christian Baber; Christine Humblet
Journal:  J Comput Aided Mol Des       Date:  2009-09-26       Impact factor: 3.686

7.  Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates.

Authors:  Yuedong Yang; Eshel Faraggi; Huiying Zhao; Yaoqi Zhou
Journal:  Bioinformatics       Date:  2011-06-11       Impact factor: 6.937

8.  (PS)2-v2: template-based protein structure prediction server.

Authors:  Chih-Chieh Chen; Jenn-Kang Hwang; Jinn-Moon Yang
Journal:  BMC Bioinformatics       Date:  2009-10-31       Impact factor: 3.169

9.  Designing succinct structural alphabets.

Authors:  Shuai Cheng Li; Dongbo Bu; Xin Gao; Jinbo Xu; Ming Li
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

10.  PSPP: a protein structure prediction pipeline for computing clusters.

Authors:  Michael S Lee; Rajkumar Bondugula; Valmik Desai; Nela Zavaljevski; In-Chul Yeh; Anders Wallqvist; Jaques Reifman
Journal:  PLoS One       Date:  2009-07-16       Impact factor: 3.240

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